Analysis of the retraining strategies for multi-label text message classification in call/contact center systems

Abstract Today, in many areas of technology, we can come across applications of various artificial intelligence methods. They usually involve models trained on some specific pool of learning data. Sometimes, however, the data analyzed by these solutions can change its nature over time. This usually results in a decrease in classification efficiency. In such a case, the use of techniques to retrain the originally trained reference models should be considered. One of the industries where the nature of data changes quite dynamically over time is the broadly defined call/contact center systems. An example of a module that is often found in this type of system and that, due to frequently changing marketing campaigns, requires the use of learning techniques is the automatic classification of text data. The paper describes the process of retraining the original reference models used in a multi-label text message classification method dedicated directly to call/contact center systems applications. In order to carry out the retraining process, Polish-language data from the actual archives of a large commercial contact center system and English-language data extracted from a publicly available database were used. The study was conducted for models based on artificial neural networks and bidirectional encoder representations from transformer type models. In addition, two different retraining strategies were studied, the results of which were compared with data obtained from the operation of reference models. As a result of the research work, an improvement of up to 5% in classification efficiency, as described by the metric Emotica was obtained, which means that proper integration of the retraining process brings tangible benefits to the solution tested in the article. Thus, it can also benefit the solutions used in business.